Cloud CAE...On-Demand Scalable Cloud CAE Private Hybrid Public Actionable Data HPC AI Stanford Conference. ... “Infinite” Hardware Many, Many Simulations Wealth of Data. ... –Docker

Cloud CAE A Game Changer for Engineers

Ian Campbell

CEO, OnScale

HPC AI Stanford Conference 2

1940’s 1960’s 1980’s 2020’s

Engineers: Making Science Fiction Reality Since 10,000 B.C.

Design Simulatefor Days/Weeks/Months…

Computer Aided Engineering

3

CAE Should Answer: “How can I engineer the BEST design?”

Analyze ResultsIdea

(Repeat over and over and over and over…)

100B Core-Hours/Year, 99% On-Premise

HPC AI Stanford Conference

The Solution:

4

Faster!Results

FREE CAE SOFTWARE

NO L ICENSES, NO IT, NO HW

Engineering Problems

Engineering Results

than Legacy

CAE Solutions

Source: 1000’s of Customer Benchmarks

SAAS, ON-DEMAND, SCALABLE

WORLD-CLASS SOLVERS

On-Demand Scalable Cloud CAE

Private

Hybrid

Public

Actionable Data


OnScale at a Glance

Founded in 2017 by world-class engineers/entrepreneurs

Venture backed and growing quickly

Focusing on Future of Engineering:


Work-

flow

Cloud

HPC

Solvers

AI

Disruptive Cloud CAE Platform

BioMed Devices ADAS Sensors/Systems

IoT Sensors 5G RF Components

Advanced Therapies Consumer Ultrasound 3D Ultrasound Driver Monitoring

Initial Applications: High Tech


Multi-Billion Dollar Markets, Big, Bursty CAE Workloads

MEMS Mics Gesture Sensors RF Filters MEMS RF Switches

The Multi-Billion Dollar Pain

7

Month 1 Month 2 Month 3 Month 4 Month 5 Month 6

Risk

Fab Device

Design

Freeze

Device

Verification

Go or

No-Go

MPSystem Verification

If “No-Go”

then Reiterate

(and spend another $1M …)

Traditional Design Cycle = Too Much Risk, Time, and Cost

CONFIDENTIAL

CAE

<10 S

ims

Device-Only CAE:

No System Insights

Up-front

CAE

Bottleneck

Cloud CAE → Minimize Risk, Time, and Cost

e

The Solution

8

Month 1 Month 2 Month 3 Month 4 Month 5 Month 6

Device

Verification Go!

>1M

Sim

s

CONFIDENTIAL

CAE

Cloud CAE Breaks

Bottleneck

Fab Device

Minimize Protos

& Short Loops

System Verification

Head Start on

Algos, FW/SW

Reduce

Time

MP

More

Design Wins!

Design

Freeze

Use Case: 5G RF Front-Ends

5

Full 3D Simulations: Impossible with On-premise CAE

Optimize Next Gen 5G

Filters in Full 3D


Use Case: Fingerprint Sensors

5

Massive Sim Studies Reduce Risk, Cost and Time to Market

Simulates Sensor AND System

Effects in Full 3D

Simulation Data Enhances AI Algos

for Fingerprint Recognition


Enhanced Engineering Workflows


Example: IoT Sensor Design Plugins

EDA for ASIC Design

MCAD for Sensor Design

Iterate

Design

Iterate

Design

EDA Plugin

CAD Plugin

Problems

Results

ProblemsResults

Data

• On-Demand HPC

• Data Storage

• Version Control

• Central Data Repo

Disruptive SaaS Sales Model

Other Benefits: No IT, No Licenses, No Hardware, One-hour Setup, Free GUI

for Model Setup & Post Processing, Free Training/Examples


Cloud CAE = No SW LicensesCloud CAE breaks licensing constraints

– Run massive numbers of sims in parallel

– Multi-tenant, supports large engineering teams


Legacy CAE

1 Engineer 1 License1 PC

1 Sim

Limited Data

Cloud CAE

Many

Engineers

“Infinite”

Hardware

Many, Many

Simulations

Wealth of Data

Unified Pricing Model


Why Solver Efficiency Matters


Low Med High Very High

Solv

e T

ime

Min

Hrs

Days

Problem Size (DoF)

OnScale Scales with Problem Size – Legacy Competition Does Not:

100k 10M 100M1M

Wks

Advantages:

• Solve Time

• Cloud Compute Cost

Limited

Practicality

(takes weeks)

High Cloud

Compute

Cost Barrier

Impossiblewith Legacy(too large for any system)

Fully Coupled Physics“Fully Coupled”: all physics algos on same finite element mesh

– Reduces CPU and RAM requirements

– Reduces overall solve time and Cloud Compute cost


Legacy CAE OnScale

Mechanical Electrical EM

Decoupled solvers, serial solve,

separate FEM matrices

FEM1 FEM2 FEM3

Mechanical

Electrical

EM

Single

FEM

Fully coupled solvers, parallel

solve, single FEM matrix

OnScale Cloud MPI Acceleration• MPI splits model across 10,000’s of cores

• Unparalleled acceleration on Cloud HPC

• Previously impossible problems now Solvable


Legacy CAE

• Not possible, too large

• Even 1M DoF problems

take hours/days

OnScale

Cores 900

Runtime 47 min

3D BAW Model, 100 MDoF

OnScale MPI Acceleration


With MPI, simulation acceleration is linear with additional cores

212

210

28

26

24

22

22 24 26 28 210 212

CPU Cores

Accele

rati

on M

ult

iple

Competition

Diminishing

Returns

Why the Time is Right

Cloud CAE is Enabled by New Cloud tech:– Kubernetes clusters (2015)

– Docker container system (2016)

– True Cloud “Bare Metal HPCs” (2017)

– Advanced token “licensing” (2017)


Big-Compute is waiting for

the Perfect Cloud CAE App– Ground-up physics coupling

– Parallelism built-in from day 1

– Move 100B core-hours to Cloud

[email protected]

Questions?

CAE is a massive HPC workload world wide

99% of of CAE is on-premise, Legacy model

In 3-5 years, majority will be on Cloud HPC


mailto:[email protected]

Documents

Cloud CAE...On-Demand Scalable Cloud CAE Private Hybrid Public Actionable Data HPC AI Stanford Conference. ... “Infinite” Hardware Many, Many Simulations Wealth of Data. ... –Docker